Machine Learning for Cyber Threat & Anomaly Detection
Completed by Krishna Kanneboina
June 25, 2026
15 hours (approximately)
Krishna Kanneboina's account is verified. Coursera certifies their successful completion of Machine Learning for Cyber Threat & Anomaly Detection
What you will learn
Evaluate the role, strengths, and limitations of ML in cybersecurity, including its vulnerability to inference and poisoning attacks.
Build and train supervised classification and regression models on real-world cybersecurity datasets to detect malware and fraud.
Apply artificial neural networks to analyse malware binaries and classify malicious behavioural patterns using real datasets.
Construct network anomaly detection models using KNN and One-Class SVM to identify outlier traffic and detect attacks.
Skills you will gain
- Category: Network Security
- Category: Fraud detection
- Category: Deep Learning
- Category: Unsupervised Learning
- Category: Machine Learning Algorithms
- Category: AI Security
- Category: Data Preprocessing
- Category: Security Management
- Category: Cyber Security Assessment
- Category: Feature Engineering
- Category: Classification Algorithms
- Category: Malware Protection
